text: /*
   * This file is part of the OpenMV project.
   * Copyright (c) 2013/2014 Ibrahim Abdelkader <i.abdalkader@gmail.com>
   * This work is licensed under the MIT license, see the file LICENSE for details.
   *
   * ORB keypoints descriptor based on OpenCV code.
   */
  
  #include <stdio.h>
  #include <string.h>
  #include <stdint.h>
  #include <stdbool.h>
  #include "fmath.h"
  #include "arm_math.h"
  #include "ff.h"
  #include "imlib.h"
  #include "xalloc.h"
  #include "fb_alloc.h"
  
  #define PATCH_SIZE  (31) // 31x31 pixels
  #define KDESC_SIZE  (32) // 32 bytes
  #define MAX_KP_DIST (KDESC_SIZE*8)
  
  typedef struct {
      int x;
      int y;
  } sample_point_t;
  
  const static int u_max[] = {
      15, 15, 15, 15, 14, 14, 14, 13,
      13, 12, 11, 10, 9, 8, 6, 3, 0
  };
  
  const static int sample_pattern[256*4] = {
      8,-3, 9,5/*mean (0), correlation (0)*/,
      4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
      -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
      7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
      2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
      1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
      -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
      -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
      -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
      10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
      -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
      -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
      7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
      -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
      -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
      -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
      12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
      -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
      -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
      11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
      4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
      5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
      3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
      -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
      -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
      -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
      -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
      -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
      -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
      5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
      5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
      1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
      9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
      4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
      2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
      -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
      -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
      4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
      0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
      -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
      -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
      -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
      8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
      0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
      7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
      -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
      10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
      -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
      10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
      -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
      -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
      3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
      5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
      -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
      3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
      2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
      -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
      -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
      -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
      -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
      6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
      -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
      -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
      -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
      3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
      -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
      -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
      2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
      -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
      -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
      5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
      -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
      -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
      -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
      10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
      7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
      -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
      -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
      7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
      -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
      -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
      -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
      7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
      -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
      1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
      2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
      -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
      -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
      7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
      1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
      9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
      -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
      -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
      7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
      12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
      6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
      5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
      2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
      3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
      2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
      9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
      -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
      -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
      1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
      6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
      2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
      6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
      3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
      7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
      -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
      -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
      -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
      -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
      8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
      4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
      -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
      4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
      -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
      -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
      7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
      -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
      -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
      8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
      -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
      1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
      7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
      -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
      11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
      -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
      3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
      5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
      0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
      -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
      0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
      -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
      5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
      3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
      -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
      -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
      -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
      6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
      -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
      -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
      1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
      4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
      -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
      2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
      -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
      4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
      -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
      -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
      7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
      4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
      -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
      7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
      7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
      -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
      -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
      -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
      2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
      10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
      -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
      8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
      2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
      -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
      -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
      -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
      5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
      -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
      -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
      -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
      -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
      -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
      2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
      -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
      -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
      -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
      -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
      6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
      -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
      11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
      7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
      -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
      -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
      -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
      -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
      -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
      -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
      -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
      -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
      1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
      1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
      9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
      5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
      -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
      -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
      -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
      -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
      8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
      2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
      7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
      -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
      -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
      4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
      3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
      -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
      5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
      4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
      -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
      0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
      -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
      3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
      -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
      8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
      -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
      2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
      10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
      6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
      -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
      -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
      -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
      -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
      -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
      4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
      2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
      6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
      3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
      11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
      -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
      4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
      2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
      -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
      -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
      -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
      6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
      0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
      -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
      -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
      -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
      5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
      2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
      -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
      9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
      11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
      3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
      -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
      3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
      -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
      5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
      8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
      7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
      -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
      7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
      9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
      7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
      -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
  };
  
  static int kpt_comp(const kp_t *kp1, const kp_t *kp2)
  {
      // Descending order
      return kp2->score - kp1->score;
  }
  
  static int comp_angle(image_t *img, kp_t *kp, float *a, float *b)
  {
      int step = img->w;
      int half_k = 31/2;
      int m_01 = 0, m_10 = 0;
      uint8_t *center = img->pixels+(kp->y*img->w+kp->x);
  
      // Treat the center line differently, v=0
      for (int u = -half_k; u <= half_k; ++u) {
          m_10 += u * center[u];
      }
  
      // Go line by line in the circular patch
      for (int v = 1; v <= half_k; ++v) {
          // Proceed over the two lines
          int v_sum = 0;
          int d = u_max[v];
          for (int u = -d; u <= d; ++u) {
              int val_plus = center[u + v*step], val_minus = center[u - v*step];
              v_sum += (val_plus - val_minus);
              m_10 += u * (val_plus + val_minus);
          }
          m_01 += v * v_sum;
      }
  
      int angle = (int) (atan2f((float)m_01, (float)m_10) * (180.0f/M_PI));
      if (angle < 0) {
          angle += 360;
      }
  
      // Quantize angle to 15 degrees
      angle = angle - (angle % 15);
  
      *a = cos_table[angle];
      *b = sin_table[angle];
      return angle;
  }
  
  static void image_scale(image_t *src, image_t *dst)
  {
      int x_ratio = (int)((src->w<<16)/dst->w) +1;
      int y_ratio = (int)((src->h<<16)/dst->h) +1;
  
  	for (int y=0; y<dst->h; y++) {
          int sy = (y*y_ratio)>>16;
  		for (int x=0; x<dst->w; x++) {
              int sx = (x*x_ratio)>>16;
  			dst->pixels[y*dst->w+x] = src->pixels[sy*src->w+sx];
  		}
  	}
  }
  
  array_t *orb_find_keypoints(image_t *img, bool normalized, int threshold,
          float scale_factor, int max_keypoints, corner_detector_t corner_detector, rectangle_t *roi)
  {
  	array_t *kpts;
  	array_alloc(&kpts, xfree);
  
      int octave = 1;
      int kpts_index = 0;
      rectangle_t roi_scaled;
  
      for(float scale=1.0f; ; scale*=scale_factor, octave++) {
          image_t img_scaled = {
              .bpp = 1,
              .w = (int) roundf(img->w/scale),
              .h = (int) roundf(img->h/scale),
              .pixels = NULL 
          };
   
          // Add patch size to ROI
          roi_scaled.x = (int) roundf(roi->x/scale) + (PATCH_SIZE);
          roi_scaled.y = (int) roundf(roi->y/scale) + (PATCH_SIZE);
          roi_scaled.w = (int) roundf(roi->w/scale) - (PATCH_SIZE*2);
          roi_scaled.h = (int) roundf(roi->h/scale) - (PATCH_SIZE*2);
  
          if (roi_scaled.w <= (PATCH_SIZE*2) ||
              roi_scaled.h <= (PATCH_SIZE*2)) {
              break;
          }
  
          img_scaled.pixels = fb_alloc(img_scaled.w * img_scaled.h);
          // Down scale image
          image_scale(img, &img_scaled);
  
          // Gaussian smooth the image before extracting keypoints
          imlib_sepconv3(&img_scaled, kernel_gauss_3, 1.0f/16.0f, 0.0f);
  
  		// Find kpts
          if (corner_detector == CORNER_FAST) {
              fast_detect(&img_scaled, kpts, threshold, &roi_scaled);
          } else {
              agast_detect(&img_scaled, kpts, threshold, &roi_scaled);
          }
  
          for (int k=kpts_index; k<array_length(kpts); k++, kpts_index++) {
              // Set keypoint octave/scale
              kp_t *kpt = array_at(kpts, k);
              kpt->octave = octave;
  
              int x, y;
              float a, b;
              sample_point_t *pattern = (sample_point_t*) sample_pattern;
              kpt->angle = comp_angle(&img_scaled, kpt, &a, &b);
  
              #if 1
              #define GET_VALUE(idx) \
                     (x = (int) roundf(pattern[idx].x*a - pattern[idx].y*b), \
                      y = (int) roundf(pattern[idx].x*b + pattern[idx].y*a), \
                      img_scaled.pixels[((kpt->y+y)*img_scaled.w)+(kpt->x+x)])
              #else
              #define GET_VALUE(idx) \
                     (img_scaled.pixels[((kpt->y+pattern[idx].y)*img_scaled.w)+(kpt->x+pattern[idx].x)])
              #endif
  
              for (int i=0; i<KDESC_SIZE; ++i, pattern+=16) {
                  int t0, t1, t2, t3, u, v, k, val;
                  t0 = GET_VALUE(0); t1 = GET_VALUE(1);
                  t2 = GET_VALUE(2); t3 = GET_VALUE(3);
                  u = 0, v = 2;
                  if( t1 > t0 ) t0 = t1, u = 1;
                  if( t3 > t2 ) t2 = t3, v = 3;
                  k = t0 > t2 ? u : v;
                  val = k;
  
                  t0 = GET_VALUE(4); t1 = GET_VALUE(5);
                  t2 = GET_VALUE(6); t3 = GET_VALUE(7);
                  u = 0, v = 2;
                  if( t1 > t0 ) t0 = t1, u = 1;
                  if( t3 > t2 ) t2 = t3, v = 3;
                  k = t0 > t2 ? u : v;
                  val |= k << 2;
  
                  t0 = GET_VALUE(8); t1 = GET_VALUE(9);
                  t2 = GET_VALUE(10); t3 = GET_VALUE(11);
                  u = 0, v = 2;
                  if( t1 > t0 ) t0 = t1, u = 1;
                  if( t3 > t2 ) t2 = t3, v = 3;
                  k = t0 > t2 ? u : v;
                  val |= k << 4;
  
                  t0 = GET_VALUE(12); t1 = GET_VALUE(13);
                  t2 = GET_VALUE(14); t3 = GET_VALUE(15);
                  u = 0, v = 2;
                  if( t1 > t0 ) t0 = t1, u = 1;
                  if( t3 > t2 ) t2 = t3, v = 3;
                  k = t0 > t2 ? u : v;
                  val |= k << 6;
  
                  kpt->desc[i] = (uint8_t) val;
              }
  
              kpt->x = (int)floorf(kpt->x * scale);
              kpt->y = (int)floorf(kpt->y * scale);
          }
  
          // Free current scale
          fb_free();
  
          if (normalized) {
              break;
          }
      }
  
      // Sort keypoints by score and return top n keypoints
      array_sort(kpts, (array_comp_t) kpt_comp);
      if (array_length(kpts) > max_keypoints) {
          array_resize(kpts, max_keypoints);
      }
  
  	return kpts;
  }
  
  // This is a modifed popcount that counts every 2 different bits as 1.
  // This is what should actually be used with wta_k == 3 or 4.
  static inline uint32_t popcount(uint32_t i)
  {
       i = i - ((i >> 1) & 0x55555555);
       i = ((i & 0xAAAAAAAA)>>1) | (i & 0x55555555);
       i = (i & 0x33333333) + ((i >> 2) & 0x33333333);
       return (((i + (i >> 4)) & 0x0F0F0F0F) * 0x01010101) >> 24;
  }
  
  static kp_t *find_best_match(kp_t *kp1, array_t *kpts, int *dist_out1, int *dist_out2)
  {
      kp_t *min_kp=NULL;
      int min_dist1 = MAX_KP_DIST;
      int min_dist2 = MAX_KP_DIST;
      int kpts_size = array_length(kpts);
  
      for (int i=0; i<kpts_size; i++) {
          int dist = 0;
          kp_t *kp2 = array_at(kpts, i);
  
          if (kp2->matched == 0) {
              for (int m=0; m<(KDESC_SIZE/4); m++) {
                  dist += popcount(((uint32_t*)(kp1->desc))[m] ^ ((uint32_t*)(kp2->desc))[m]);
              }
  
              if (dist < min_dist1) {
                  min_kp = kp2;
                  min_dist2 = min_dist1;
                  min_dist1 = dist;
              }
          }
      }
  
      *dist_out1 = min_dist1;
      *dist_out2 = min_dist2;
      return min_kp;
  }
  
  int orb_match_keypoints(array_t *kpts1, array_t *kpts2, int threshold, rectangle_t *r, point_t *c, int *angle)
  {
      int matches=0;
      int cx = 0, cy = 0;
      uint16_t angles[360]={0};
      int kpts1_size = array_length(kpts1);
  
      r->w = r->h = 0;
      r->x = r->y = 20000;
  
      // Match keypoints and find "good matches" This runs 2/3 tests found in the RobustMatcher from the OpenCV programming cookbook.
      // The first test is based on the distance ratio between the two best matches for a feature, to remove ambiguous matches.
      // Second test is the symmetry test (corss-matching) both points in a match must be the best matching feature of each other.
      for (int i=0; i<kpts1_size; i++) {
          int min_dist1 = 0;
          int min_dist2 = 0;
          kp_t *min_kp = NULL;
          kp_t *kp1 = array_at(kpts1, i);
  
          // Find the best match in second set
          min_kp = find_best_match(kp1, kpts2, &min_dist1, &min_dist2);
          // Test the distance ratio between the best two matches
          if ((min_dist1*100/min_dist2) > threshold) {
              continue;
          }
  
          // Cross-match the keypoint in the first set
          kp_t *kp2 = find_best_match(min_kp, kpts1, &min_dist1, &min_dist2);
          // Test the distance ratio between the best two matches
          if ((min_dist1*100/min_dist2) > threshold) {
              continue;
          }
  
          // Cross-match test
          if (kp1 == kp2) {
              int x, y;
              matches++;
              min_kp->matched = 1;
              cx += x = min_kp->x;
              cy += y = min_kp->y;
              rectangle_expand(r, x, y);
              int angle = (int) abs(min_kp->angle-kp1->angle);
              if (angle >= 0 && angle < 360) {
                  angles[angle]++;
              }
          }
      }
  
      if (matches == 0) {
          r->x = r->y = 0;
          return 0;
      }
  
      // Fix centroid x/y
      c->x = cx/matches;
      c->y = cy/matches;
  
      // Fix rectangle w/h
      r->w = r->w - r->x;
      r->h = r->h - r->y;
  
      int max_angle = 0;
      for (int i=0; i<360; i++) {
          if (angles[i] > max_angle) {
              max_angle = angles[i];
              *angle = i;
          }
      }
  
      return matches;
  }
  
  int orb_filter_keypoints(array_t *kpts, rectangle_t *r, point_t *c)
  {
      int matches=0;
      int cx = 0, cy = 0;
      int kpts_size = array_length(kpts);
  
      r->w = r->h = 0;
      r->x = r->y = 20000;
  
      float *kpts_dist = fb_alloc(kpts_size * sizeof(float));
  
      // Find centroid 
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = array_at(kpts, i);
          if (kp->matched) {
              matches ++;
              cx += kp->x;
              cy += kp->y;
          }
      }
  
      // Centroid
      cx /= matches;
      cy /= matches;
  
      // Find mean distance from centroid
      float mdist = 0.0f;
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = array_at(kpts, i);
          if (kp->matched) {
              kpts_dist[i] = orb_cluster_dist(cx, cy, kp);
              mdist += kpts_dist[i];
          }
      }
      // Mean distance from centroid
      mdist /= matches;
  
      // Find variance
      float var = 0.0f;
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = array_at(kpts, i);
          if (kp->matched) {
              float dist = kpts_dist[i];
              var += (mdist - dist) * (mdist - dist);
          }
      }
  
      // Find standard deviation
      float stdist = fast_sqrtf(var/matches);
      
      // Reset centroid
      matches = 0;
      cx = cy = 0;
  
      // Remove outliers and get new centroid
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = array_at(kpts, i);
          if (kp->matched) {
              float dist = fabs(mdist - kpts_dist[i]);
              if (dist > stdist) {
                  kp->matched = 0;
              } else {
                  int x, y;
                  matches++;
                  cx += x = kp->x;
                  cy += y = kp->y;
                  rectangle_expand(r, x, y);
              }
          }
      }
  
      // Fix centroid x/y
      c->x = cx/matches;
      c->y = cy/matches;
  
      // Fix rectangle w/h
      r->w = r->w - r->x;
      r->h = r->h - r->y;
  
      // Free distance array
      fb_free();
      return matches;
  }
  
  int orb_save_descriptor(FIL *fp, array_t *kpts)
  {
      UINT bytes;
      FRESULT res;
  
      int kpts_size = array_length(kpts);
  
      // Write the number of keypoints
      res = f_write(fp, &kpts_size, sizeof(kpts_size), &bytes);
      if (res != FR_OK || bytes != sizeof(kpts_size)) {
          goto error;
      }
  
      // Write keypoints
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = array_at(kpts, i);
  
          // Write X
          res = f_write(fp, &kp->x, sizeof(kp->x), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->x)) {
              goto error;
          }
  
          // Write Y
          res = f_write(fp, &kp->y, sizeof(kp->y), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->y)) {
              goto error;
          }
  
          // Write Score
          res = f_write(fp, &kp->score, sizeof(kp->score), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->score)) {
              goto error;
          }
  
          // Write Octave
          res = f_write(fp, &kp->octave, sizeof(kp->octave), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->octave)) {
              goto error;
          }
  
          // Write Angle
          res = f_write(fp, &kp->angle, sizeof(kp->angle), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->angle)) {
              goto error;
          }
  
          // Write descriptor
          res = f_write(fp, kp->desc, KDESC_SIZE, &bytes);
          if (res != FR_OK || bytes != KDESC_SIZE) {
              goto error;
          }
      }
  
  error:
      return res;
  }
  
  int orb_load_descriptor(FIL *fp, array_t *kpts)
  {
      UINT bytes;
      FRESULT res=FR_OK;
  
      int kpts_size=0;
  
      // Read number of keypoints
      res = f_read(fp, &kpts_size, sizeof(kpts_size), &bytes);
      if (res != FR_OK || bytes != sizeof(kpts_size)) {
          goto error;
      }
  
      // Read keypoints
      for (int i=0; i<kpts_size; i++) {
          kp_t *kp = xalloc(sizeof(*kp));
          kp->matched = 0;
  
          // Read X
          res = f_read(fp, &kp->x, sizeof(kp->x), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->x)) {
              goto error;
          }
  
          // Read Y
          res = f_read(fp, &kp->y, sizeof(kp->y), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->y)) {
              goto error;
          }
  
          // Read Score
          res = f_read(fp, &kp->score, sizeof(kp->score), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->score)) {
              goto error;
          }
  
          // Read Octave
          res = f_read(fp, &kp->octave, sizeof(kp->octave), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->octave)) {
              goto error;
          }
  
          // Read Angle
          res = f_read(fp, &kp->angle, sizeof(kp->angle), &bytes);
          if (res != FR_OK || bytes != sizeof(kp->angle)) {
              goto error;
          }
  
          // Read descriptor
          res = f_read(fp, kp->desc,  KDESC_SIZE, &bytes);
          if (res != FR_OK || bytes != KDESC_SIZE) {
              goto error;
          }
  
          // Add keypoint to array
          array_push_back(kpts, kp);
      }
  
  error:
      return res;
  }
  
  float orb_cluster_dist(int cx, int cy, void *kp_in)
  {
      float sum=0.0f;
      kp_t *kp = kp_in;
      sum += (cx - kp->x) * (cx - kp->x);
      sum += (cy - kp->y) * (cy - kp->y);
      return fast_sqrtf(sum);
  
  }
  